Aligning Developmental and Processing Accounts of Implicit and Statistical Learning

A long‐standing question in child language research concerns how children achieve mature syntactic knowledge in the face of a complex linguistic environment. A widely accepted view is that this process involves extracting distributional regularities from the environment in a manner that is incidental and happens, for the most part, without the learner's awareness. In this way, the debate speaks to two associated but separate literatures in language acquisition: statistical learning and implicit learning. Both fields have explored this issue in some depth but, at present, neither the results from the infant studies used by the statistical learning literature nor the artificial grammar learning tasks studies from the implicit learning literature can be used to fully explain how children's syntax becomes adult‐like. In this work, we consider an alternative explanation—that children use error‐based learning to become mature syntax users. We discuss this proposal in the light of the behavioral findings from structural priming studies and the computational findings from Chang, Dell, and Bock's (2006) dual‐path model, which incorporates properties from both statistical and implicit learning, and offers an explanation for syntax learning and structural priming using a common error‐based learning mechanism. We then turn our attention to future directions for the field, here suggesting how structural priming might inform the statistical learning and implicit learning literature on the nature of the learning mechanism.     

呼吸内科PBL教学法在全科医师规培中的应用

目的探索基于呼吸治疗团队教学的以问题为基础的教学法(problem based learning,PBL)在全科住院医师规范化培训中的应用。方法呼吸系统以老年人慢性疾病为主,是全科医学生教育的重要环节。开展PBL教学法的教学探索,以期达到对全科医师的专科疾病培训,使其理解并掌握相关疾病知识,能适应未来医学发展和社会需要,为患者提供更好的社区医疗服务。结果PBL教学法的精髓是发挥问题对学习过程的指导作用,调动学生学习的积极性和主动性,整体提升学生的综合素质和能力。结论在全科医学规范化培训教学中运用PBL教学,带教老师以任务为主线、以学员为主体进行引导,可极大的提高学生的学习兴趣、主动性以及自学能力,从而培养学员的分析问题、解决问题的能力。可以更好的帮助学员形成以患者为中心的临床诊断思路、提高解决临床问题的综合能力及团队合作能力。     

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer

Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.     

大补元煎通过上调BDNF/TrkB/CREB信号通路改善APP/PS1双转基因痴呆小鼠海马突触可塑性

目的:观察大补元煎对APP/PS1痴呆小鼠海马突触可塑性及脑源性神经营养因子(BDNF)/酪氨酸蛋白激酶受体B(TrkB)/环磷酸腺苷反应元件结合蛋白(CREB)信号通路的作用,并探讨其改善突触可塑性的可能机制。方法:将APP/PS1小鼠36只分为模型组、多奈哌齐组(6.5×10~(-4)g·kg~(-1)·d~(-1))和大补元煎组(13.2 g·kg~(-1)·d~(-1)),野生鼠12只设为正常组,正常组和模型组给予等体积生理盐水,各组连续灌胃30 d。应用Morris水迷宫检测各组小鼠的学习记忆能力,应用尼氏染色和高尔基染色观察海马区神经元和突触的病理形态变化,应用免疫荧光(IF)观察海马突触后致密蛋白95(PSD95)及突触素(SYN)的蛋白表达水平,采用蛋白免疫印迹法(Western blot)检测海马中BDNF,TrkB,CREB及磷酸化CREB(p-CREB)的蛋白表达水平。结果:与空白组比较,模型组小鼠平台潜伏期和游泳总路程增加(P0.01),穿越平台次数和目标象限停留时间减少(P0.01),小鼠海马CA3区神经元胞内尼氏体减少或消失,小鼠海马CA3区神经元及树突分支数量、树突棘密度减少(P0.01),小鼠海马SYN,PSD95,BDNF,TrkB及p-CREB的蛋白表达水平减少(P0.01)。与模型组比较,多奈哌齐组和大补元煎组小鼠平台潜伏期和游泳总路程减少(P0.05,P0.01),穿越平台次数和目标象限停留时间增加(P0.05,P0.01),小鼠海马CA3区神经元胞内尼氏体数量增多,小鼠海马CA3区神经元及树突分支数量,树突棘密度增加(P0.05,P0.01),小鼠海马SYN,PSD95,BDNF,TrkB及p-CREB的蛋白表达水平增加(P0.05,P0.01)。结论:大补元煎改善APP/PS1双转基因小鼠突触可塑性的机制可能与其上调小鼠海马中BDNF/TrkB/CREB信号通路有关。     

Biohorology and biomarkers of aging: Current state-of-the-art,challenges and opportunities

The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology — the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.     

机器学习算法在麻醉领域的应用

人工智能在医疗领域的应用逐步推动医疗变革,机器学习算法融入麻醉领域对于进一步提升麻醉发展意义重大。为了解机器学习算法在麻醉领域的应用现况及推动相关研究进展,本文总结了机器学习在围手术期麻醉管理和预测术后并发症方面的应用、基本概念和研究现状,指出了目前机器学习算法在医疗领域的不足之处。